Coactive learning with a human expert for robotic information gathering

Somers, T.; Hollinger, Geoffrey A.

doi:10.1109/icra.2015.7139234

2015 IEEE International Conference on Robotics and Automation (ICRA) 2015

DOI: 10.1109/icra.2015.7139234

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Coactive learning with a human expert for robotic information gathering

T. Somers

Geoffrey A. Hollinger

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2015

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Cited by 2 publications

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Human–robot planning and learning for marine data collection

Somers

Hollinger

2015

Auton Robot

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We propose an integrated learning and planning framework that leverages knowledge from a human user along with prior information about the environment to generate trajectories for scientific data collection in marine environments. The proposed framework combines principles from probabilistic planning with nonparametric uncertainty modeling to refine trajectories for execution by autonomous vehicles. These trajectories are informed by a utility function learned from the human operator's implicit preferences using a modified coactive learning algorithm. The resulting techniques allow for user-specified trajectories to be modified for reduced risk of collision and increased reliability. We test our approach in two marine monitoring domains and show that the proposed framework mimics human-planned trajectories while also reducing the risk of operation. This work provides insight into the tools necessary for combining human input with vehicle navigation to provide persistent autonomy.

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Human–robot planning and learning for marine data collection

Somers

Hollinger

2015

Auton Robot

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Coactive Learning

Shivaswamy

Joachims

2015

jair

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We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. Interactions in the Coactive Learning model take the following form: at each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking); the user responds by correcting the system if necessary, providing a slightly improved but not necessarily optimal object as feedback. We argue that such preference feedback can be inferred in large quantity from observable user behavior (e.g., clicks in web search), unlike the optimal feedback required in the expert model or the cardinal valuations required for bandit learning. Despite the relaxed requirements for the feedback, we show that it is possible to adapt many existing online learning algorithms to the coactive framework. In particular, we provide algorithms that achieve square root regret in terms of cardinal utility, even though the learning algorithm never observes cardinal utility values directly. We also provide an algorithm with logarithmic regret in the case of strongly convex loss functions. An extensive empirical study demonstrates the applicability of our model and algorithms on a movie recommendation task, as well as ranking for web search.

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Coactive learning with a human expert for robotic information gathering

Cited by 2 publications

References 12 publications

Human–robot planning and learning for marine data collection

Human–robot planning and learning for marine data collection

Coactive Learning

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